AUTHOR=Xu Fangyi , Zhu Wenchao , Shen Yao , Wang Jian , Xu Rui , Outesh Chooah , Song Lijiang , Gan Yi , Pu Cailing , Hu Hongjie 

TITLE=Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma

JOURNAL=Frontiers in Oncology

VOLUME=Volume 10 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.00872

DOI=10.3389/fonc.2020.00872

ISSN=2234-943X

ABSTRACT=Objectives: To investigate the performance of radiomic-based quantitative analysis on CT images in identifying invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).Methods: 275 lung adenocarcinoma cases, with a total of 322 pGGNs resected surgically and confirmed pathologically, from January 2015 to October 2017 were enrolled in this retrospective study. Radiomic feature extraction was performed using Pyradiomics with semi-automatically segmented tumor regions on CT scans which were contoured with an inhouse plugin for 3D-Slicer. Random forest and Support Vector Machine (SVM) were used for feature selection and predictive model building. The predictive performance of each model was evaluated through the receiver operating characteristic curve (ROC).Results: Among 322 nodules, 150(46.6%) were Adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and 172(53.4%) were invasive adenocarcinoma (IVA). All nodules were split into training and test cohort randomly with a ratio of 4:1 to establish predictive models. Three different predictive models containing conventional, radiomic and combined models were created using training cohort. The area under the curve (AUC) values in test cohort were 0.866(0.778~0.954) for combined model with 79.69%, 88.24%, 70.00%, 84.00% and 76.92% for accuracy, sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) respectively.The combined model created in our study showed significant predictive power with good accuracy and sensitivity which provided a non-invasive, convenient, economic and repeatable way for the identification of IVA from AIS/MIA representing as pGGNs.